Multiple-cause discovery combined with structure learning for high-dimensional discrete data and application to stock prediction

Causal discovery in observational data is crucial to a variety of scientific and business research. Although many causal discovery algorithms have been proposed in recent decades, none of them is effective enough in dealing with high-dimensional discrete data. The main challenge is the complex interactions among large volume of variables, leading to numerous spurious causalities found. In this work, we propose a novel multiple-cause discovery method combined with structure learning (McDSL) to eliminate the spurious causalities. The method is carried out in two phases. In the first phase, conditional independence test is used to distinguish direct causal candidates from the indirect ones. In the second phase, causal direction of multi-cause structure is carefully determined with a hybrid causal discovery method. Validation experiments on synthetic data showed that McDSL is reliable in discovering multi-cause structures and eliminating indirect causes. We then applied this algorithm in discovering multiple causes of stock return based on 13-year historical financial data of the Shanghai Stock Exchanges of China, and established a stock prediction model. Experimental results showed that the McDSL discovered causes revealed changes of key risk factors of the stock market over 13 years, which indicated investors should change their investment strategy over time. Moreover, the causes discovered by McDSL have better performance in predicting stock return than that of other common filter-based feature selection algorithms.